Positron emission tomography (PET) is a medical imaging technique that provides in vivo functional information on physiological processes. In PET, positron-emitting radionuclides on biologically active molecules, such as fluorodeoxyglucose (FDG), are introduced into a patient. A radionuclide in the patient's body decays and emits a positron, which undergoes an annihilation with a nearby electron, subsequently generating a pair of 511 keV gamma ray photons that travel in nearly opposite directions. If a pair of gamma ray photons are detected within a coincidence timing window by two in an array of PET detectors, a coincidence event is recorded for the line of response (LOR), which is a line between the two detectors that have detected the photon pair. Each event is sorted in an array called a sinogram.
PET image reconstruction is used to reconstruct the three-dimensional distribution of the radiotracer in the patient's body from the measured sinogram data. The spatial distribution of the radiotracer is called an activity or emission image, or simply an image, and an estimate of the unknown true image provided by an image reconstruction process is called a reconstructed image.
Reconstructed PET images are used for quantitative analysis. Standardized uptake value (SUV), often used for a simple semi-quantitative analysis, provides clinically important information for diagnosis, staging and monitoring response to treatment of cancer. The SUV may be calculated as a maximum voxel value (SUVmax) or a mean over a region of interest (SUVmean or SUVpeak). However, PET imaging systems have finite spatial image resolution limited by the detector design and also by the image reconstruction process. The finite image resolution of a PET imaging system causes bias errors in region of interest (ROI) quantitation, which are called partial volume errors (PVEs). For accurate and consistent quantitation, PVE may be appropriately corrected. However, partial volume correction (PVC) for penalized-likelihood image reconstruction is difficult because PVE is affected by a number of factors including patient size, photon count, ROI location, background activity and reconstruction parameters in complex ways.